Building Capacity for Supply-Side Modeling, Simulation, and Research:

1. Building Capacity for Supply-Side Modeling, Simulation, and Research: An Example Using HCUP Data to Improve Timeliness of Estimates September 21, 2011 Claudia Steiner, M.D, M.P.H.

2. What is HCUP? • HCUP is • Longitudinal Multi-Year and All-Payer, Inpatient, Emergency Department , and Ambulatory Surgery Databases based onHospital Billing Data.

3. The Foundation of HCUP Data is Hospital Billing Data Demographic Data Diagnoses Procedures Charges

4. The HCUP Partnership State Federal Industry

5. Partnership: HCUP Database Participation By State WA VT MT ND ME NH MN SD OR ID WI MA NY MI WY RI NV IA PA IN OH CT NE UT IL NJ CO WV CA VA DE KS MO KY MD NC AR TN OK AZ NM SC GA MS AL LA TX FL HI AK AK Non-participating Partners Providing Inpatient Data Only Partners Providing Inpatient & Ambulatory Surgery Data Partners Providing Inpatient & Emergency Department Data Partners Providing Inpatient, Ambulatory Surgery,& Emergency Department Data

6. HCUP Has Six Types of Databases • Three state-level databases State Ambulatory Surgery Databases (SASD) State Inpatient Databases (SID) State Emergency Department Databases (SEDD) 6

7. HCUP Has Six Types of Databases • Three nationwide databases Kids’ Inpatient Database (KID) Nationwide Inpatient Sample (NIS) Nationwide Emergency Department Sample (NEDS) 7

8. What Types of Care Are and Are Not Captured by HCUP?

9. Where Do We Get HCUP Data? 14% (N=805) HCUP data is mostly from community hospitals Typically not included in HCUP data 86% (N=5,010) Included in HCUP data Source: American Hospital Association (AHA), 2008

10. What Are Community Hospitals? American Hospital Association Definition: Non-Federal, short-term, general, and other specialty hospitals, excluding hospital units of other institutions (e.g., prisons)

11. Accelerating HCUP Data and Information • Need for timely projections on trends • Provide analysts and policy makers timely information that can be used to evaluate the impact of quality improvement efforts • HCUP Nationwide Inpatient Sample (NIS) typically lags the current calendar year by 17 months • 2009 NIS available in June 2011 • Demonstrate feasibility of producing gap-year national estimates • Demonstrate feasibility of collecting and processing quarterly data

12. Which HCUP Partners Collect Quarterly Data? • A total of 40 of 44 States (91%) reported that they collect data at more frequent intervals than annually: • 23 States collect quarterly data (AR, CT, FL, GA, HI, IA, IL, IN, KY, MA, ME, MD, MI, MN, MO, MT, NC, NE, NM, NY, OH, OR, PA, RI, TN, TX, UT, VA, VT, WI & WY) • 4 States collect monthly data (NJ, SC, WA & WV) • 3 States collect both quarterly and monthly data (CO, NH & NV) • 2 State collects semi-annual data (AZ, CA) • Four of the 44 States do not collect data more frequently than annually: Kansas, Louisiana, Oklahoma, and South Dakota.

13. HCUP Data for TimelyNational Projections • Factors that contribute to success of the initiative: • Longitudinal nature of the HCUP databases • 1988 forward • Breadth of data across 44 states • 295 million inpatient discharges from the 2001 to 2009 • Capacity of states to produce early quarterly data • Modeling expertise at AHRQ and contract staff • Availability of SAS Econometric Time Series Software • Leveraging of report technology developed under the NHQR

14. Selected HAIs and Outcomes • Eight HAIs selected; six reported separately for adults and pediatrics • The HAIs reported in this study may have originated from either inpatient or outpatient health care services • HAIs are identified by a principal or secondary diagnosis on an inpatient stay • Indication that the diagnosis was present on admission (POA) could not be considered because POA is not available in historical SID • Approach provides nationwide, population-based prevalence instead of the hospital-based performance or accountability measures

15. Five Outcomes of Interest • Projections focus on hospital utilization and outcomes: • Number of inpatient discharges • Rate per 1,000 discharges • Average total charge (includes hospital services, no professional fees, not inflation-adjusted) • Average length of stay • In-hospital mortality rate

16. Postoperative Sepsis (Adult) Population at risk: Elective, non-maternal, adult, surgical discharges with a length of stay >= four days, excluding discharges with any diagnosis of immunocompromised state, discharges with any diagnosis of cancer, and discharges with a principal diagnosis of infection

17. Postoperative Sepsis (Pediatric) Population at risk: Non-neonatal, pediatric, surgical discharges with a length of stay >= four days, excluding discharges with a principal diagnosis of infection or a DRG indicating surgery for likely infection

18. Clostridium Difficile Infections (Adult) Population at risk: Non-maternal, adult discharges

19. Clostridium Difficile Infections (Pediatric) Population at risk: Pediatric discharges

20. HCUP Data for TimelyNational Projections • HCUP projections in newest report are based on: • 295 million inpatient discharges from the 2001 to 2009 HCUP SID • “Early” 2010 data from 14 selected HCUP Statesthat submitted data by July 2011 • Ten cardiovascular / cerebrovascular conditions and procedures selected • Each stratified by adult age (18-44, 45-64, 65+) and gender

21. Five Outcomes of Interest • Projections focus on hospital utilization and outcomes: • Number of inpatient discharges • Average total cost (includes hospital services, no professional fees, not inflation-adjusted) • Average length of stay • In-hospital mortality rate

22. Acute Myocardial Infarction(Adult Age Group)

23. Acute Myocardial Infarction(Gender)

24. HCUP Data Mining • Purpose: Use early 2010 State Inpatient Data to identify diagnoses and procedures for which observed outcomes in 2010 digressed substantially from those outcomes predicted for 2010 using historical data from 2001 - 2009. • Method: Analyze normalized residuals to identify the 2010 residuals that were statistical outliers compared with residuals observed during the 2001 - 2009 baseline period. These outlier residuals indicate potentially radical changes to the established trend for the outcome under consideration.

25. Procedure Categories with Substantial Deviations Between Actual vs. Expected

26. Questions?

27. Healthcare Cost and Utilization Project (HCUP)